02-AREAS

This guide outlines how to create efficient agentic systems by focusing on three layers: tools, reasoning, and action. Each layer presents unique challenges that can impact overall system performance.

The Automated Design of Agentic Systems (ADAS) is a unique approach in AI that enables the creation of agents capable of designing, testing, and refining themselves.

Explore essential AI tools for R programming to enhance your low-code development capabilities and streamline your projects. | Restackio

Introduction. Few days ago I discovered the OpenAI Gym library which is a bunch of standardized AI environments written in Python to benchmark the skills of AI programs and scripts, aka agents.

Like many of you, my first real exposure to AI was when ChatGPT dropped. I spent way too much time prompting it with random stuff, used it for some PowerShell, and tried out the voice feature when that launched. Mostly, I’ve used AI for things like writing docs, double-checking my grammar and English, and making some funny pictures.

Let’s start this with some confessions: I’m at best an enthusiastic amateur with AI. I know more than most, and I know nothing in the grand scheme. Example: I’m not sure I have any idea of what an AI agent is. I think I do, but there’s so much marketing hype around them that I

Use Meta Prompting to rapidly generate results in the GenAI Age - README.md

Generating diagrams with AI / LLMs

Replace 'hub' with 'ingest' in any GitHub URL for a prompt-friendly text.

A flexible, feature-rich yet light-weight logging framework based on R6 classes. It supports hierarchical loggers, custom log levels, arbitrary data fields in log events, logging to plaintext, JSON, (rotating) files, memory buffers. For extra appenders that support logging to databases, email and push notifications see the the package lgr.app.

Explore essential logging practices for AI applications to ensure compliance and enhance software reliability. | Restackio

Please remember you are using a beta version of mcpx and mcp.run services.

Hi Obsidian Community! https://github.com/PublikPrinciple/obsidian-mcp-rest I’m working on implementing an MCP (Model Context Protocol) server that integrates with Obsidian’s Local REST API plugin. The goal is to allow AI assistants like Claude to interact directly with Obsidian vaults in a secure, local manner. Project Overview GitHub Repository: GitHub - PublikPrinciple/obsidian-mcp-rest: An MCP server implementation for accessing Obsidian via local REST API Purpose: Enable AI assistants ...

Do you need to add LLM capabilities to your R scripts and applications? Here are three tools you'll want to know.

We’re evolving our platform to help developers and enterprises build useful and reliable agents.

Natural language processing has come a long way since its foundations were laid in the 1940s and 50s (for an introduction see, e.g., Jurafsky and Martin (2008, 2009, 2022 draft third edition): Speech and Language Processing, Pearson Prentice Hall). This CRAN task view collects relevant R packages that support computational linguists in conducting analysis of speech and language on a variety of levels - setting focus on words, syntax, semantics, and pragmatics.

If you are looking to use Natural Language Processing in R applications, these are some of the best NLP packages you must know.

Learn about Claude Code, an agentic coding tool made by Anthropic. Currently in beta as a research preview.

The R task Force - R experts for all your needs

Get started building your own server to use in Claude for Desktop and other clients.

Anthropic developed the Model Context Protocol (MCP), an open standard that connects AI assistants to systems where data actually lives—content repositories, business tools, development environments, and more.

Introducing agent mode for GitHub Copilot in VS Code, announcing the general availability of Copilot Edits, and providing a first look at our SWE agent.

Learn about generative AI workflows in Vertex AI, available models (including Gemini), and how to start building your generative AI app.

An artificial intelligence (AI) agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system.

Support chat with Large Language Models. Contribute to choonghyunryu/chat2doc development by creating an account on GitHub.

testthat-style LLM evaluation for R. Contribute to simonpcouch/evalthat development by creating an account on GitHub.

In this blog post, we’ll explore how AI Models Are Learning to Do Instead of Just Say. We will explain how function calling works, its real-world applications, and how you can implement it using tools like Ollama and Llama 3.2. Whether you’re a developer looking to build AI-powered applications or simply curious about how AI is transforming the way we interact with APIs, this guide will walk you through everything you need to know.

Provides a number of utilities for describing R objects and package documentation in plain text. For interactive use, this is especially powerful for describing relevant pieces of context to large language models. When used programmatically, these utilities can be registered with ellmer chats as tool calls, enabling language models to peruse package documentation and explore your computational environment.